Cook Richard J, Lawless Jerald F
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1 Canada.
Jpn J Stat Data Sci. 2025;8(1):323-345. doi: 10.1007/s42081-024-00276-9. Epub 2024 Oct 30.
Cohort studies of disease processes deal with events and other outcomes that may occur in individuals following disease onset. The particular goals are often the evaluation of interventions and estimation of the effects of risk factors that may affect the disease course. Models and methods of event history analysis and longitudinal data analysis provide tools for understanding disease processes, but there are numerous challenges in practice. These are related to the complexity of the disease processes and to the difficulty of recruiting representative individuals and acquiring detailed longitudinal data on their disease course. Our objectives here are to describe some of these challenges and to review methods of addressing them. We emphasize the appeal of multistate models as a framework for understanding both disease processes and the processes governing recruitment of individuals for cohort studies and the collection of data. The use of other observational data sources in order to enhance model fitting and analysis is discussed.
疾病过程的队列研究涉及疾病发作后个体可能发生的事件及其他结果。其特定目标通常是评估干预措施以及估计可能影响疾病进程的风险因素的作用。事件史分析和纵向数据分析的模型与方法为理解疾病过程提供了工具，但在实践中存在诸多挑战。这些挑战与疾病过程的复杂性以及招募具有代表性个体并获取其疾病进程详细纵向数据的难度有关。我们在此的目标是描述其中一些挑战并回顾应对这些挑战的方法。我们强调多状态模型作为理解疾病过程以及队列研究中个体招募和数据收集所涉及过程的框架的吸引力。还讨论了使用其他观测数据源以增强模型拟合和分析的情况。
